Research Article

Robo academic advisor: Can chatbots and artificial intelligence replace human interaction?

Mohammed Muneerali Thottoli 1 * , Badria Hamed Alruqaishi 1 , Arockiasamy Soosaimanickam 1
More Detail
1 University of Nizwa, Nizwa, OMAN* Corresponding Author
Contemporary Educational Technology, 16(1), January 2024, ep485, https://doi.org/10.30935/cedtech/13948
Published Online: 01 December 2023, Published: 01 January 2024
OPEN ACCESS   2994 Views   2484 Downloads
Download Full Text (PDF)

ABSTRACT

Purpose: Chatbots and artificial intelligence (AI) have the potential to alleviate some of the challenges faced by humans. Faculties frequently swamped with teaching and research may find it difficult to act in a parental role for students by offering them individualized advice. Hence, the primary purpose of this study is to review the literature on chatbots and AI in light of their role in auto-advising systems. The authors aimed to gain insights into the most pertinent topics and concerns related to robo academic advisor and identify any gaps in the literature that could serve as potential avenues for further research.
Design/methodology/approach: The research employs a systematic literature review and bibliometric techniques to find 67 primary papers that have been published between 1984 and 2023. Using the Scopus database, the researchers built a summary of the literature on chatbots and AI in academic advice.
Findings: Chatbot applications can be a promising approach to address the challenges of balancing personalized student advising with automation. More empirical research is required, especially on chatbots and other AI-based advising systems, to understand their effectiveness and how they can be integrated into educational settings.
Research limitations/implications: This research’s sample size may restrict its findings’ generalizability. Furthermore, the study’s focus on chatbots may overlook the potential benefits of other AI technologies in enhancing robo academic advising systems. Future research could explore the impact of robo academic advisors in diverse societal backgrounds to gain a more comprehensive understanding of their implications.
Practical implications: Higher educational institutions (HEIs) should establish a robo academic advising system that serves various stakeholders. The system’s chatbots and AI features must be user-friendly, considering the customers’ familiarity with robots.
Originality/value: This study contributes to a better understanding of HEIs’ perceptions of the adoption of chatbots and AI in academic advising by providing insightful information about the main forces behind robo academic advising, illuminating the most frequently studied uses of chatbots and AI in academic advising.
Keywords: Artificial intelligence, chatbots, robo, academic advisor, human interaction

CITATION (APA)

Thottoli, M. M., Alruqaishi, B. H., & Soosaimanickam, A. (2024). Robo academic advisor: Can chatbots and artificial intelligence replace human interaction?. Contemporary Educational Technology, 16(1), ep485. https://doi.org/10.30935/cedtech/13948

REFERENCES

  1. Abdelhamid, A. A., & Alotaibi, S. R. (2021). Adaptive multi-agent smart academic advising framework. IET Software, 15(5), 293-307. https://doi.org/10.1049/sfw2.12021
  2. Agbo, F. J., Oyelere, S. S., Suhonen, J., & Tukiainen, M. (2021). Scientific production and thematic breakthroughs in smart learning environments: A bibliometric analysis. Smart Learning Environments, 8, 1. https://doi.org/10.1186/s40561-020-00145-4
  3. Al Mazroui, T. S. S., Thottoli, M. M., Al Alawi, M. M. S., Al Shukaili, N. T. H., & Al Hoqani, D. S. A. (2023). Bibliometric analysis of value-added tax research: A comparative study between European Union and Gulf Cooperation Council countries. International Journal of Law and Management, 65(6), 560-585. https://doi.org/10.1108/IJLMA-01-2023-0016
  4. Alblawi, A. S., & Alhamed, A. A. (2017). Big data and learning analytics in higher education: Demystifying variety, acquisition, storage, NLP and analytics. In Proceedings of the 2017 IEEE Conference on Big Data and Analytics (pp. 124-129). IEEE. https://doi.org/10.1109/ICBDAA.2017.8284118
  5. Alfarsi, G. M. S., OMAR, K. A. M., & Alsinani, M. J. (2017). A rule-based system for advising undergraduate students. Journal of Theoretical & Applied Information Technology, 95(11), 2453-2465.
  6. Almutawah, K. A. (2014). A decision support system for academic advisors. International Journal of Business Information Systems, 16(2), 177-195. https://doi.org/10.1504/IJBIS.2014.062837
  7. Alneyadi, S., & Wardat, Y. (2023). ChatGPT: Revolutionizing student achievement in the electronic magnetism unit for eleventh-grade students in Emirates schools. Contemporary Educational Technology, 15(4), ep448. https://doi.org/10.30935/cedtech/13417
  8. Aly, W. M., Eskaf, K. A., & Selim, A. S. (2017). Fuzzy mobile expert system for academic advising. In Proceedings of the 2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (pp. 1-5). IEEE. https://doi.org/10.1109/CCECE.2017.7946846
  9. Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959-975. https://doi.org/10.1016/j.joi.2017.08.007
  10. Asakiewicz, C., Stohr, E. A., Mahajan, S., & Pandey, L. (2017). Building a cognitive application using Watson DeepQA. IT Professional, 19(4), 36-44. https://doi.org/10.1109/MITP.2017.3051322
  11. Assiri, A., Al-Ghamdi, A. A. M., & Bredesen, H. (2020). From traditional to intelligent academic advising: A systematic literature review of e-academic advising. International Journal of Advanced Computer Science and Applications, 11(4). https://doi.org/10.14569/IJACSA.2020.0110467
  12. Berse, S., Akca, K., Dirgar, E., & Kaplan Serin, E. (2023). The role and potential contributions of the artificial intelligence language model ChatGPT. Annals of Biomedical Engineering. https://doi.org/10.1007/s10439-023-03296-w
  13. Bihari, A., Dash, M., Kar, S. K., Muduli, K., Kumar, A., & Luthra, S. (2022). Exploring behavioral bias affecting investment decision-making: A network cluster based conceptual analysis for future research. International Journal of Industrial Engineering and Operations Management, 4(1/2), 19-43. https://doi.org/10.1108/IJIEOM-08-2022-0033
  14. Biletskiy, Y., Brown, J. A., & Ranganathan, G. (2009). Information extraction from syllabi for academic e-advising. Expert Systems with Applications, 36(3), 4508-4516. https://doi.org/10.1016/j.eswa.2008.05.011
  15. Bilquise, G., & Shaalan, K. (2022). AI-based academic advising framework: A knowledge management perspective. International Journal of Advanced Computer Science and Applications, 13(8). https://doi.org/10.14569/IJACSA.2022.0130823
  16. Bilquise, G., Ibrahim, S., & Shaalan, K. (2022). Bilingual AI-driven chatbot for academic advising. International Journal of Advanced Computer Science and Applications, 13(8). https://doi.org/10.14569/IJACSA.2022.0130808
  17. Bogicevic, V., Yang, W., Bilgihan, A., & Bujisic, M. (2013). Airport service quality drivers of passenger satisfaction. Tourism Review, 68(4), 3-18. https://doi.org/10.1108/TR-09-2013-0047
  18. Castellano, E. J., Martinez, L. U. I. S., & Sánchez, P. J. (2008). OrieB, a linguistic CRS for supporting decision making in academic orientation. In Computational intelligence in decision and control (pp. 841-846). https://doi.org/10.1142/9789812799470_0138
  19. Chan, Z. C., Chan, H. Y., Chow, H. C. J., Choy, S. N., Ng, K. Y., Wong, K. Y., & Yu, P. K. (2019). Academic advising in undergraduate education: A systematic review. Nurse Education Today, 75, 58-74. https://doi.org/10.1016/j.nedt.2019.01.009
  20. Dinić, B. M., & Jevremov, T. (2021). Trends in research related to the Dark Triad: A bibliometric analysis. Current Psychology, 40, 3206-3215. https://doi.org/10.1007/s12144-019-00250-9
  21. Engin, G., Aksoyer, B., Avdagic, M., Bozanlı, D., Hanay, U., Maden, D., & Ertek, G. (2014). Rule-based expert systems for supporting university students. Procedia Computer Science, 31, 22-31. https://doi.org/10.1016/j.procs.2014.05.241
  22. Esteban, A., Zafra, A., & Romero, C. (2020). Helping university students to choose elective courses by using a hybrid multi-criteria recommendation system with genetic optimization. Knowledge-Based Systems, 194, 105385. https://doi.org/10.1016/j.knosys.2019.105385
  23. Falagas, M. E., Karavasiou, A. I., & Bliziotis, I. A. (2006). A bibliometric analysis of global trends of research productivity in tropical medicine. Acta Tropica, 99(2-3), 155-159. https://doi.org/10.1016/j.actatropica.2006.07.011
  24. Fayoumi, A. G., & Hajjar, A. F. (2020). Advanced learning analytics in academic education: Academic performance forecasting based on an artificial neural network. International Journal on Semantic Web and Information Systems, 16(3), 70-87. https://doi.org/10.4018/IJSWIS.2020070105
  25. Fu, X., Niu, Z., & Yeh, M. K. (2016). Research trends in sustainable operation: A bibliographic coupling clustering analysis from 1988 to 2016. Cluster Computing, 19, 2211-2223. https://doi.org/10.1007/s10586-016-0624-3
  26. Gnana Rajesh, D., Tamilarasi, G., & Khan, M. E. (2023). Voice and text-based virtual assistant for academic advising using knowledge-based intelligent decision support expert system. In Proceedings of ICSADL 2022: Sentiment Analysis and Deep Learning (pp. 483-491). Springer. https://doi.org/10.1007/978-981-19-5443-6_36
  27. Golumbic, M. C., Markovich, M., Tsur, S., & Schild, U. J. (1986). A knowledge-based expert system for student advising. IEEE Transactions on Education, E29(2), 120-124. https://doi.org/10.1109/TE.1986.5570612
  28. Goyal, K., & Kumar, S. (2021). Financial literacy: A systematic review and bibliometric analysis. International Journal of Consumer Studies, 45(1), 80-105. https://doi.org/10.1111/ijcs.12605
  29. Gutiérrez, F., Seipp, K., Ochoa, X., Chiluiza, K., De Laet, T., & Verbert, K. (2020). LADA: A learning analytics dashboard for academic advising. Computers in Human Behavior, 107, 105826. https://doi.org/10.1016/j.chb.2018.12.004
  30. Hagemann, N., O’Mahony, M. P., & Smyth, B. (2023). Visual module exploration: A live-user evaluation. KI-Künstliche Intelligenz [Artificial Intelligence]. https://doi.org/10.1007/s13218-023-00800-1
  31. Haluza, D., & Jungwirth, D. (2023). Artificial intelligence and ten societal megatrends: An exploratory study using GPT-3. Systems, 11(3), 120. https://doi.org/10.3390/systems11030120
  32. Hilliger, I., De Laet, T., Henríquez, V., Guerra, J., Ortiz-Rojas, M., Zuñiga, M. Á., Baier, J., & Pérez-Sanagustín, M. (2020). For learners, with learners: Identifying indicators for an academic advising dashboard for students. In Proceedings of the 15th European Conference on Technology Enhanced Learning (pp. 117-130). Springer. https://doi.org/10.1007/978-3-030-57717-9_9
  33. Ho, C. C., Lee, H. L., Lo, W. K., & Lui, K. F. A. (2018). Developing a chatbot for college student program advisement. In Proceedings of the 2018 International Symposium on Educational Technology (pp. 52-56). IEEE.
  34. Hwang, G. J., Xie, H., Wah, B. W., & Gašević, D. (2020). Vision, challenges, roles and research issues of artificial intelligence in education. Computers and Education: Artificial Intelligence, 1, 100001. https://doi.org/10.1016/j.caeai.2020.100001
  35. Ismail, H., Hussein, N., Elabyad, R., & Said, S. (2021). A serverless academic adviser chatbot. In Proceedings of the 7th Annual International Conference on Arab Women in Computing in Conjunction with the 2nd Forum of Women in Research (pp. 1-5). https://doi.org/10.1145/3485557.3485587
  36. Jackson, P., & Lefrere, P. (1984). On the application of rule-based techniques to the design of advice-giving systems. International Journal of Man-Machine Studies, 20(1), 63-86. https://doi.org/10.1016/S0020-7373(84)80006-1
  37. Kessler, M. M. (1963). Bibliographic coupling between scientific papers. American Documentation, 14(1), 10-25. https://doi.org/10.1002/asi.5090140103
  38. Khalil, O. E. (1993). Artificial decision-making and artificial ethics: A management concern. Journal of Business Ethics, 12, 313-321. https://doi.org/10.1007/BF01666535
  39. Kuhail, M. A., Al Katheeri, H., Negreiros, J., Seffah, A., & Alfandi, O. (2022a). Engaging students with a chatbot-based academic advising system. International Journal of Human-Computer Interaction, 39(10), 2115-2141. https://doi.org/10.1080/10447318.2022.2074645
  40. Kuhail, M. A., Thomas, J., Alramlawi, S., Shah, S. J. H., & Thornquist, E. (2022b). Interacting with a chatbot-based advising system: Understanding the effect of chatbot personality and user gender on behavior. Informatics, 9(4), 81. https://doi.org/10.3390/informatics9040081
  41. Lee, T., Zhu, T., Liu, S., Trac, L., Huang, Z., & Chen, Y. (2021). CASExplorer: A conversational academic and career advisor for college students. In Proceedings of the 9th International Symposium of Chinese CHI (pp. 112-116). https://doi.org/10.1145/3490355.3490368
  42. Lim, M. S., Ho, S. B., & Chai, I. (2021). Design and functionality of a university academic advisor chatbot as an early intervention to improve students’ academic performance. In Proceedings of the Computational Science and Technology (pp. 167-178). Springer. https://doi.org/10.1007/978-981-33-4069-5_15
  43. Lin, Y., & Yu, Z. (2023). A bibliometric analysis of artificial intelligence chatbots in educational contexts. Interactive Technology and Smart Education. https://doi.org/10.1108/ITSE-12-2022-0165
  44. Lizarraga, C., Aguayo, R., Quiñonez, Y., Reyes, V., & Mejia, J. (2022). A new proposal for virtual academic advisories using chatbots. In Proceedings of the 11th International Conference on Software Process Improvement (pp. 233-242). Springer. https://doi.org/10.1007/978-3-031-20322-0_16
  45. Loucif, S., Gassoumi, L., & Negreiros, J. (2020). Considering students’ abilities in the academic advising process. Education Sciences, 10(9), 254. https://doi.org/10.3390/educsci10090254
  46. Mariani, M. M., Machado, I., Magrelli, V., & Dwivedi, Y. K. (2022). Artificial intelligence in innovation research: A systematic review, conceptual framework, and future research directions. Technovation, 122, 102623. https://doi.org/10.1016/j.technovation.2022.102623
  47. McCavit, K., & Zellner, N. E. B. (2016). Persistence of physics and engineering students via peer mentoring, active learning, and intentional advising. European Journal of Physics, 37(6), 065702. https://doi.org/10.1088/0143-0807/37/6/065702
  48. Meho, L. I., & Yang, K. (2007). Impact of data sources on citation counts and rankings of LIS faculty: Web of Science versus Scopus and Google Scholar. Journal of the American Society for Information Science and Technology, 58(13), 2105-2125. https://doi.org/10.1002/asi.20677
  49. Nam, S., & Samson, P. (2019). Integrating students’ behavioral signals and academic profiles in early warning system. In Proceedings of the 20th International Conference on Artificial Intelligence in Education (pp. 345-357). Springer. https://doi.org/10.1007/978-3-030-23204-7_29
  50. Nanibaa’, A. G., Sathe, N. A., Antommaria, A. H. M., Holm, I. A., Sanderson, S. C., Smith, M. E., McPheeters, M. L., & Clayton, E. W. (2016). A systematic literature review of individuals’ perspectives on broad consent and data sharing in the United States. Genetics in Medicine, 18(7), 663-671. https://doi.org/10.1038/gim.2015.138
  51. Nelekar, S., Abdulrahman, A., Gupta, M., & Richards, D. (2022). Effectiveness of embodied conversational agents for managing academic stress at an Indian university (ARU) during COVID-19. British Journal of Educational Technology, 53(3), 491-511. https://doi.org/10.1111/bjet.13174
  52. Noruzi, A., & Abdekhoda, M. (2014). Scientometric analysis of Iraqi-Kurdistan universities’ scientific productivity. The Electronic Library, 32(6), 770-785. https://doi.org/10.1108/EL-01-2013-0004
  53. Okonkwo, C. W., & Ade-Ibijola, A. (2021). Chatbots applications in education: A systematic review. Computers and Education: Artificial Intelligence, 2, 100033. https://doi.org/10.1016/j.caeai.2021.100033
  54. Omotehinwa, T. O. (2022). Examining the developments in scheduling algorithms research: A bibliometric approach. Heliyon, 8(5), e09510. https://doi.org/10.1016/j.heliyon.2022.e09510
  55. Pattnaik, D., Kumar, S., & Vashishtha, A. (2020). Research on trade credit–A systematic review and bibliometric analysis. Qualitative Research in Financial Markets, 12(4), 367-390. https://doi.org/10.1108/QRFM-09-2019-0103
  56. Roessger, K. M., Eisentrout, K., & Hevel, M. S. (2019). Age and academic advising in community colleges: Examining the assumption of self-directed learning. Community College Journal of Research and Practice, 43(6), 441-454. https://doi.org/10.1080/10668926.2018.1490669
  57. Rogers, G., Szomszor, M., & Adams, J. (2020). Sample size in bibliometric analysis. Scientometrics, 125(1), 777-794. https://doi.org/10.1007/s11192-020-03647-7
  58. Rojas-Sánchez, M. A., Palos-Sánchez, P. R., & Folgado-Fernández, J. A. (2023). Systematic literature review and bibliometric analysis on virtual reality and education. Education and Information Technologies, 28(1), 155-192. https://doi.org/10.1007/s10639-022-11167-5
  59. Saquib, S., & Ali, R. (2017). Understanding dynamics of trending topics in Twitter. In Proceedings of the 2017 International Conference on Computing, Communication and Automation (pp. 98-103). IEEE. https://doi.org/10.1109/CCAA.2017.8229780
  60. Shavetov, S., Borisov, O., Borisova, E., & Zhivitskii, A. (2022). Student advising services in control systems and robotics. IFAC-PapersOnLine, 55(17), 13-18. https://doi.org/10.1016/j.ifacol.2022.09.218
  61. Sim, J. (1998). Collecting and analyzing qualitative data: Issues raised by the focus group. Journal of Advanced Nursing, 28(2), 345-352. https://doi.org/10.1046/j.1365-2648.1998.00692.x
  62. Sivaram, M., Shanmugapriya, S., Yuvaraj, D., Porkodi, V., Akbari, A., Hashim, W., Maseleno, A., & Huda, M. (2020). Decision support system for determining academic advisor using simple additive weighting. In Proceedings of the Cognitive Informatics and Soft Computing (pp. 149-156). Springer. https://doi.org/10.1007/978-981-15-1451-7_16
  63. Tawafak, R. M., Alfarsi, G., Romli, A., Jabbar, J., Malik, S. I., & Alsideiri, A. (2020). A review paper on student-graduate advisory expert system. In Proceedings of the 2020 International Conference on Computing and Information Technology (pp. 1-5). IEEE. https://doi.org/10.1109/ICCIT-144147971.2020.9213794
  64. Thottoli, M. M., Islam, M., Sobhani, F. A., Rahman, S., & Hassan, M. (2022). Auditing and sustainability accounting: A global examination using the Scopus database. Sustainability, 14(23), 16323. https://doi.org/10.3390/su142316323
  65. Wardat, Y., Tashtoush, M. A., AlAli, R., & Jarrah, A. M. (2023). ChatGPT: A revolutionary tool for teaching and learning mathematics. Eurasia Journal of Mathematics, Science and Technology Education, 19(7), em2286. https://doi.org/10.29333/ejmste/13272
  66. Young, M., St-Onge, C., Xiao, J., Vachon Lachiver, E., & Torabi, N. (2018). Characterizing the literature on validity and assessment in medical education: A bibliometric study. Perspectives on Medical Education, 7, 182-191. https://doi.org/10.1007/S40037-018-0433-X
  67. Zhao, Z., Liu, W., Qian, Y., Nie, L., Yin, Y., & Zhang, Y. (2018). Identifying advisor-advisee relationships from co-author networks via a novel deep model. Information Sciences, 466, 258-269. https://doi.org/10.1016/j.ins.2018.07.064
  68. Zheng, M., Fu, H. Z., & Ho, Y. S. (2017). Research trends and hotspots related to ammonia oxidation based on bibliometric analysis. Environmental Science and Pollution Research, 24, 20409-20421. https://doi.org/10.1007/s11356-017-9711-0
  69. Zupic, I., & Čater, T. (2015). Bibliometric methods in management and organization. Organizational Research Methods, 18(3), 429-472. https://doi.org/10.1177/1094428114562629